For this challenge, we use SBERT that improves upon BERT and RoBERTa, by optimizing semantic textual similarity using sentence-level embedding from siamese and triplet network structure [1]}. This allows the model to derive fixed-size vectors for input sentences, and are much more efficient to scale. In our study, posts corresponding to pre and post COVID-19, along with the vocabulary for each Topic identified in Table REF  was fed into the SBERT network to derive sentence-embeddings.
Once we derive semantic vector embeddings, we compare pre and post text embeddings with embeddings generated from words for each Topic in Table REF  using the notion of Cosine similarity [2]}. The output of this step essentially gives a notion of similarity between the two embeddings compared. For every text, the topic which it belongs to was identified as the one for whose embedding, the similarity with the embedding of the corresponding text was identified as the highest.
